Many different types of data compression have been developed during the past half century to facilitate electronic data transmission and electronic data storage. Many data-compression methods are lossless, in that, absent errors, decompression of data compressed by lossless compression techniques returns the original data. Many other compression methods are referred to as “lossy,” because the methods obtain compression at the expense of loss of a portion of the original information content of the data that is compressed. Examples of lossless data compression include various types of entropy coding, including Huffman encoding and run-length encoding, which more efficiently encode the original data. Examples of lossy compression methods include the quantization of transform coefficients and resolution-decimation steps undertaken in MPEG compression of video signals.
Many of the well-known data-compression techniques can be used to compress data or a data stream prior to transmission from a data source to a receiving entity. These compression techniques generally seek to identify and remove redundant data from a given signal or stream and/or to remove unneeded information from the data signal or data stream, and, by doing so, reduce the amount of data transmitted from the data source to the receiving entity. Data compression can therefore increase communications bandwidths and decrease data-transmission overheads and delays at the expense of the computational overhead of compression, on the data-source side, and the computational expense of compressed-data decompression, on the receiving-entity side. In many distributed systems, the computational bandwidth may greatly exceed the data-transmission bandwidth, so that data-compression produces increased data-transmission bandwidth with negligible cost
Networks of low-power-consuming sensors represent a new type of distributed computing system. The low-powered sensors have significantly different characteristics and constraints than personal computers in a networked-computer system, as a result of which methods used advantageously in distributed computer networks may not provide the same advantages in low-power-consuming sensor networks. Designers, manufacturers, and users of sensors therefore continue to seek to improve and extend sensor capabilities by using sensor-applicable methods.